3 Easy Ways to Compare Two Pandas DataFrames

Python is a high-level, interpreted programming language recognized for its simplicity and readability. Created with the aid of Guido van Rossum and primarily released in 1991, Python emphasizes code clarity through its smooth syntax and indentation shape. It supports more than one programming paradigm, such as procedural, object-oriented, and useful programming. Its dynamic typing and automated memory management contribute to its ease of use, permitting fast development and prototyping. Python's network-pushed development and open-source nature make certain continuous development and substantial adoption across numerous industries.

Understanding Pandas DataFrames

Pandas DataFrames are middle facts shaped in Python for information manipulation and analysis, specifically in statistics technology. They offer a handy manner of saving and operating on two-dimensional, categorized information.

Key Features of DataFrames

Tabular Data Structure: DataFrames are two-dimensional, classified statistics systems with columns of doubtlessly differing types, comparable to a desk in a relational database or an Excel spreadsheet.
Labeled Axes: Rows and columns are each labeled, considering smooth get right of entry to, manipulation, and analysis of facts using these labels.
Alignment and Arithmetic: Automatic alignment of statistics for arithmetic operations, which simplifies running with information from one-of-a-kind assets.
Size-Mutable: DataFrames can be resized without problems, with columns and rows being delivered or removed as wished.

Common Operations

Creation: DataFrames can be made of a few information sources such as dictionaries, lists, dictionaries of lists, and outside records sources like CSV documents, SQL databases, or Excel files.
Data Selection: Subsets of data may be decided on the usage of labels (loc) or indices (iloc). The conditional choice is likewise supported.
Data Manipulation: Columns and rows can be brought, deleted, or changed. Functions are available for sorting, filtering, and grouping records.
Data Aggregation: Built-in features permit for summarizing facts through operations like suggest, sum, median, and custom aggregation capabilities.
Merging and Joining: DataFrames can be blended using concatenation, merging, and joining operations, allowing the combination of statistics from more than one asset.
Handling Missing Data: Functions are provided for detecting, doing away with, or filling in lacking information, ensuring information integrity and completeness.
Data Transformation: Operations along with applying capabilities to columns, reshaping statistics (e.g., pivoting), and changing the datatypes of columns are supported.
Input and Output: DataFrames can be studied from and written to diverse record formats, which include CSV, Excel, SQL, and more, facilitating clean information import and export.

Comparing Two Pandas DataFrames

Comparing DataFrames is a common task when you want to check for variations, ensure consistency, or validate statistical alterations.

Using `equals()` Method
Using `compare()` Method
Using '==' Operator and `any()` Method

Using `equals()` Method

The `equals()` technique checks if two DataFrames are identical. It returns a single boolean value:`True` if the DataFrames are equal and `False` in any other case.

Example

 
import pandas as pd
# Create two DataFrames
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df3 = pd.DataFrame({'A': [1, 2, 4], 'B': [4, 5, 6]})
# Compare DataFrames using equals()
print(df1.equals(df2))  
print(df1.equals(df3))     

Output:

 
True
False

Explanation

`df1.equals(df2)` returns `True` because `df1` and `df2` are identical.
`df1.equals(df3)` returns `False` because `df1` and `df3` have distinctive values in column 'A'.

Using `compare()` Method

The `compare()` method returns the differences between DataFrames. It returns a DataFrame that highlights the differences by displaying only the differing factors and values in each DataFrame.

Example

 
import pandas as pd
# Create two DataFrames
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [1, 2, 4], 'B': [4, 7, 6]})
# Compare DataFrames using compare()
diff = df1.compare(df2)
print(diff)   

Output:

 
    A          B      
  self other self other
1  NaN   NaN  5.0   7.0
2  3.0   4.0  NaN   NaN

Explanation

`df1.compare(df2)` returns a DataFrame that indicates the variations:
Row 2 has unique values in column 'A' (`3` in `df1` vs `4` in `df2`) and column 'B' (`5` in `df1` vs `7` in `df2`).

Using `==` Operator and `any()` Method

Using the '==' operator followed by `any()` facilitates identifying whether there are any differences between two DataFrames. The '==' operator creates a DataFrame of boolean values indicating detail-smart contrast. Using `any()` (with the `axis` parameter) checks if there are any `False` values indicating variations.

Example

 
import pandas as pd
# Create two DataFrames
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [1, 2, 4], 'B': [4, 7, 6]})
# Compare DataFrames using == and any()
comparison = df1 == df2
print(comparison)
# Check if there are any differences
any_differences = comparison.any().any()
print(not any_differences)     

Output:

 
      A      B
0   True   True
1   True  False
2  False   True
False

Explanation

`df1 df2` creates a DataFrame of boolean values in which `True` indicates matching values and `False` indicates variations.
Using `comparison.any().any()` checks for any `False` values. If any variations exist, it's going to go back to `False`.

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